# ML AI Project Hub

> Project hub for ML AI repositories, architecture notes, and implementation status.

- [Project Hub](/docs/): Entry point for ML AI project documentation, including repository overviews, implementation status, architecture summaries, and contributor navigation.

## Project Hub

> Entry point for ML AI project documentation, including repository overviews, implementation status, architecture summaries, and contributor navigation.

- [About MLAI](/docs/about-mlai/): Background on the ML AI project hub, what repositories it tracks, and how contributors can use this site for status and architecture context.
- [Projects](/docs/projects/): Catalog of active ML AI repositories with concise summaries of scope, focus areas, and links to deeper project-specific documentation pages.
- [OmekaRapper](/docs/omekarapper/): Detailed OmekaRapper project page covering current scope, integration model, provider architecture, and practical implementation constraints.
- [OpenSift](/docs/opensift/): Detailed OpenSift project page summarizing capabilities, maturity level, provider workflows, and currently documented hardening posture.
- [OpenContractRx](/docs/opencontractrx/): Detailed OpenContractRx page outlining healthcare contract-intelligence goals, monorepo architecture, and currently documented integration surfaces.
- [Aegis](/docs/aegis/): Detailed Aegis workspace page covering draft-stage status, protocol-centric architecture, and explicit threat-model and security-posture framing.
- [Build Principles](/docs/build-principles/): Cross-project build principles used across ML AI repositories, including status transparency, composable architecture, and security-first design practices.
- [Direction](/docs/direction/): Current direction and priorities for evolving the ML AI project hub and related repositories while keeping status and architecture documentation aligned.
- [Contribute / Contact](/docs/contribute-/-contact/): Contribution and contact guide describing where to open issues, discuss implementation details, and collaborate across ML AI repositories.